Equipment joint control Internet of Things system and method for gas safety pressure regulation and medium

Abstract

The invention provides an equipment joint control Internet of Things system and method for gas safety pressure regulation and a medium, and the Internet of Things system comprises a government safety supervision management platform, a government safety supervision sensing network platform, a government safety supervision object platform, a gas company sensing network platform, and a gas equipment object platform. The government safety supervision object platform comprises a gas company management platform. The method is executed by a gas company management platform, and comprises the following steps: acquiring historical sensing data of a sensing device; determining multiple groups of sensing statistical data based on the historical sensing data; and determining an adjustment instruction based on the current sensing data and the sensing statistical data, the adjustment instruction being configured to adjust a cooling parameter and a pressure adjustment parameter. The method can also be operated after a computer instruction stored in a computer readable storage medium is read. The invention relates to the technical field of fuel gas pressure regulation, and can realize equipment joint control and ensure efficient operation of a fuel gas pressure regulation station by timely regulating pressure regulation parameters and cooling parameters.

Description

Technical Field

[0001] This specification relates to the technical field of gas pressure regulation, and particularly to an equipment interlocking Internet of Things system, method, and medium for gas safety pressure regulation. Background Art

[0002] To ensure that the gas meets the standards and the transportation is safe, before the gas enters the downstream pipeline, it is necessary to regulate the pressure of the transported gas through a gas pressure regulating station. During the pressure regulation process, the change in gas pressure often leads to changes in the temperature of the equipment and the gas. By jointly monitoring the gas pressure, gas temperature, and equipment temperature, the interlocking control of the pressure regulating equipment and cold energy recovery equipment in the gas pressure regulating station can be achieved, and then the cold energy recovery and utilization can be realized, which is beneficial to saving energy. However, currently, there is a lack of effective means for how to adjust the pressure regulating parameters of the pressure regulating equipment and the cooling parameters of the cold energy recovery equipment in real time during the gas pressure regulation process, and to achieve the interlocking control of the pressure regulating equipment and the cold energy recovery equipment.

[0003] Therefore, it is necessary to provide an equipment interlocking Internet of Things system, method, and medium for gas safety pressure regulation, which can ensure the efficient operation of the gas pressure regulating station and the safe and stable transportation of gas by timely adjusting the pressure regulating parameters of the pressure regulating equipment and the cooling parameters of the cold energy recovery equipment. Summary of the Invention

[0004] In order to solve the problems such as how to adjust the working parameters of the pressure regulating equipment and the cold energy recovery equipment in real time during the gas pressure regulation process, the present invention provides an equipment interlocking Internet of Things system, method, and medium for gas safety pressure regulation.

[0005] The summary of the invention includes an equipment interlocking Internet of Things system for gas safety pressure regulation, which includes a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision object platform, a gas company sensor network platform, and a gas equipment object platform. The government safety supervision object platform includes a gas company management platform; the gas equipment object platform includes a pressure regulating equipment and a cold energy recovery equipment, and a sensing device is assembled on the pressure regulating equipment and the cold energy recovery equipment. The pressure regulating equipment is arranged in the gas pressure regulating station; the gas company management platform is configured to execute an equipment interlocking method for gas safety pressure regulation.

[0006] The invention content includes a method for joint control of equipment for gas safety pressure regulation, which is executed by a gas company management platform. The method includes: obtaining historical sensing data of a sensing device, where the historical sensing data includes sensing data at multiple historical moments, and the sensing data includes the first pressure of the gas before passing through the pressure regulation device, the second pressure of the gas after passing through the pressure regulation device, the gas temperature, and the device temperature of the pressure regulation device; determining multiple sets of sensing statistical data based on the historical sensing data, where the sensing statistical data includes a differential pressure statistic and a temperature difference statistic; and determining an adjustment instruction based on the current sensing data and the sensing statistical data, where the adjustment instruction is configured to adjust the cooling parameters of the cold energy recovery device and the pressure regulation parameters of the pressure regulation device.

[0007] The invention content includes a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the medium, the computer executes a method for joint control of equipment for gas safety pressure regulation.

[0008] The beneficial effects brought by the above invention content include but are not limited to: (1) By statistically analyzing the historical sensing data, sensing statistical data can be determined, and then the variation laws of the gas pressure, device temperature, and gas temperature can be evaluated; by grouping the sensing statistical data, the gas can be divided into different intervals for reference, which is convenient for performing pressure regulation operations and cooling operations on different degrees of gas with different pressures, so that the pressure regulation device and the cold energy recovery device can maintain good working efficiency; (2) Adjusting the first time interval according to the number of input pipelines can flexibly adapt to gas pressure regulation stations of different scales and complexities, significantly improving the overall efficiency and stability of the gas pressure regulation system; (3) According to the first pressure, target output pressure, and target gas temperature of the current gas pressure regulation station, an appropriate second time interval can be determined to timely adjust the working parameters of the equipment at an appropriate time, reducing the risk of failures in the gas pipeline network. Brief Description of the Drawings

[0009] This specification will further illustrate by way of exemplary embodiments, and these exemplary embodiments will be described in detail through the drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where: Figure 1 is a schematic diagram of the platform structure of an Internet of Things system for joint control of equipment for gas safety pressure regulation shown in some embodiments of this specification; Figure 2 is an exemplary flowchart of a method for joint control of equipment for gas safety pressure regulation shown in some embodiments of this specification; Figure 3 is an exemplary flowchart of determining multiple sets of sensing statistical data shown in some embodiments of this specification; Figure 4 It is an exemplary flowchart for determining the voltage regulation parameters at the first moment as shown in some embodiments of this specification; Figure 5 It is an exemplary schematic diagram of the pressure difference model as shown in some embodiments of this specification. Detailed implementation manners

[0010] The accompanying drawings required for the description of the embodiments will be briefly introduced below. The accompanying drawings do not represent all the implementation manners. The "system", "device", "unit" and / or "module" used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. If other words can achieve the same purpose, the said words can be replaced by other expressions.

[0011] Unless the context clearly indicates an exceptional situation, words such as "a", "an", "one" and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0012] When the operations are described step by step in the present invention, unless otherwise specified, the order of the steps can be adjusted, the steps can be omitted, and other steps can also be included during the operation process.

[0013] Figure 1 It is a schematic diagram of the platform structure of the equipment control Internet of Things system for gas safety voltage regulation as shown in some embodiments of this specification.

[0014] In some embodiments, as Figure 1 shown, the equipment control Internet of Things system 100 for gas safety voltage regulation may include a government safety supervision management platform 110, a government safety supervision sensing network platform 120, a government safety supervision object platform 130, a gas company sensing network platform 140, and a gas equipment object platform 150.

[0015] The government safety supervision management platform 110 refers to a platform for supervising and safely managing the gas pipeline network.

[0016] The government safety supervision sensing network platform 120 refers to a functional platform for managing the sensing communication of the government and can be configured as a communication network or a gateway, etc.

[0017] In some embodiments, the government safety supervision sensing network platform 120 can interact with the government safety supervision management platform 110 upwards and with the government safety supervision object platform 130 downwards. For example, the government safety supervision object platform 130 can send a reporting instruction to the government safety supervision management platform 110 through the government safety supervision sensing network platform 120.

[0018] The government safety supervision object platform 130 refers to an object platform for generating sensing information and executing control information.

[0019] In some embodiments, the government safety supervision object platform 130 can include a gas company management platform 131.

[0020] The gas company management platform 131 refers to a comprehensive management platform for the relevant information of the gas company.

[0021] In some embodiments, the gas company management platform 131 can include a processor. In some embodiments, the processor can include one or more sub-processing devices (e.g., a single-core processing device or a multi-core multi-chip processing device). By way of example only, the processor can include a central processing unit (CPU), an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a field-programmable gate array (FPGA), a programmable logic device (PLD), etc. or any combination thereof.

[0022] The gas company sensing network platform 140 refers to a comprehensive management platform for the sensing information of the gas company and can be configured as a communication network or a gateway, etc.

[0023] In some embodiments, the gas company sensing network platform 140 can interact with the government safety supervision object platform 130 upwards and with the gas equipment object platform 150 downwards. For example, the government safety supervision object platform 130 can send an obtained adjustment instruction to the gas equipment object platform 150 through the gas company sensing network platform 140.

[0024] The gas equipment object platform 150 refers to a functional platform for performing temperature and pressure monitoring and equipment parameter adjustment. In some embodiments, the gas equipment object platform 150 can include a pressure regulating device and a cold energy recovery device. Sensing devices are assembled on the pressure regulating device and the cold energy recovery device, and the pressure regulating device is arranged in a gas pressure regulating station.

[0025] The pressure regulating device refers to a device that can regulate the gas pressure. In some embodiments, the gas equipment object platform can further include multiple input pipelines of the gas pressure regulating station, and the pressure regulating device can include a distribution device, a pressure boosting device, and / or a pressure reducing device.

[0026] A gas pressure regulating station refers to a platform for regulating the pressure of gas. In some embodiments, a gas pressure regulating station may include an input pipeline, pressure regulating equipment, etc.

[0027] An input pipeline refers to a pipeline for transporting gas into a gas pressure regulating station.

[0028] A distribution device refers to a device for distributing gas to different paths. The distribution device has multiple functions such as flow control, pressure balance, and path selection, and is used to distribute gas from different input pipelines to different processing paths (such as a pressure boosting path, a pressure reducing path, or a direct output path) according to certain rules or ratios. In some embodiments, the distribution device may be a distribution valve, which distributes the gas from multiple input pipelines to the pressure boosting device and the pressure reducing device.

[0029] A pressure boosting device refers to a device for increasing the pressure of gas. For example, a compressor, etc. When the pressure of the gas transported by the input pipeline is lower than the target output pressure, the pressure boosting device will be activated to increase the pressure of the gas by increasing the energy of the gas (such as compressing the volume of the gas) to meet the subsequent gas transportation requirements or usage requirements.

[0030] A pressure reducing device refers to a device for reducing the pressure of gas. For example, a pressure regulating valve. When the pressure of the gas transported by the input pipeline is higher than the target output pressure or when it is necessary to reduce the gas pressure for safety, efficiency, etc., the pressure reducing device will be activated to reduce the pressure of the gas by reducing the energy of the gas (such as expanding the volume of the gas) to ensure that the gas can be safely and stably transported to the gas pipeline network.

[0031] A cold energy recovery device refers to a device for recovering refrigerant. Among them, the refrigerant refers to a medium that can lower the temperature. For example, propane, cooling water, etc. In some embodiments, the cold energy recovery device may include a refrigerant circulation pipeline. The refrigerant circulation pipeline refers to a pipeline through which the refrigerant circulates or passes.

[0032] A sensing device refers to a device for monitoring sensing data of gas or equipment, and may include a pressure sensor, a temperature sensor, etc. In some embodiments, the sensing device can be set at appropriate positions on the gas pipeline, pressure regulating equipment, and cold energy recovery equipment according to monitoring requirements. For example, pressure sensors and temperature sensors can be set near the inlet or outlet of the pressure regulating equipment, and temperature sensors can be set on the outer surface of the pressure regulating equipment, etc.

[0033] In some embodiments, the cold energy recovery device may further include an expansion refrigeration device.

[0034] An expansion refrigeration device refers to a device that uses the cold energy released during the pressure reduction and expansion process of gas to provide a refrigeration effect. For example, a turboexpander, a piston expander, etc.

[0035] In some embodiments, the expansion refrigeration device can be connected to the pressure reducing device. The expansion refrigeration device directly converts the gas pressure energy generated by the pressure reducing device into cold energy without consuming additional energy, thereby achieving energy-saving and efficient refrigeration.

[0036] In some embodiments of the present specification, based on the equipment joint control Internet of Things system for gas safety pressure regulation, an information operation closed loop can be formed between various functional platforms, and coordinated and regularly operated under the unified management of the gas company management platform, thereby realizing the informatization and intelligence of smart gas pressure regulation and temperature regulation.

[0037] Figure 2 is an exemplary flow chart of a device joint control method for gas safety pressure regulation according to some embodiments of this specification. Figure 2 As shown, the process 200 includes the following steps S210 to S230.

[0038] In some embodiments, the process 200 may be executed by the gas company management platform 131 , for example, by a processor in the gas company management platform 131 .

[0039] Step S210, acquiring historical sensing data of the sensing device.

[0040] The sensor data refers to data related to gas pressure regulation obtained by the sensor device. In some embodiments, the sensor data may include a first pressure of the gas before passing through the pressure regulating device, a second pressure of the gas after passing through the pressure regulating device, the gas temperature, and the device temperature of the pressure regulating device.

[0041] The first pressure and the second pressure refer to the gas pressure before and after being regulated by the pressure regulating equipment, respectively.

[0042] Gas temperature refers to the temperature of the gas after it passes through the pressure regulating equipment.

[0043] Equipment temperature refers to the temperature of the voltage regulating equipment itself.

[0044] In some embodiments, the sensing data can be acquired by real-time monitoring of the sensing device. For example, the first pressure is acquired by a pressure sensor deployed at or near the inlet of the pressure regulating device, the second pressure and the gas temperature are acquired by a pressure sensor and a temperature sensor deployed at or near the outlet of the pressure regulating device, and the device temperature is acquired by a temperature sensor deployed on the outer surface of the pressure regulating device.

[0045] In some embodiments, the historical sensor data may include sensor data of multiple historical moments. The historical moments refer to moments far before the current moment, for example, multiple moments in a day a week ago.

[0046] For example, historical sensor data can be expressed in the following table (1): Table (1)

[0047] Wherein, represents the nth historical moment, , , , respectively represent the historical first pressure, historical second pressure, historical gas temperature, and historical equipment temperature at the historical moment .

[0048] In some embodiments, the sensing data is monitored and acquired by a sensing device and uploaded to the gas company management platform through the gas company sensing network platform, and the processor can directly retrieve the historical sensing data.

[0049] Step S220, based on the historical sensing data, determine multiple sets of sensing statistical data.

[0050] The sensing statistical data refers to the statistical data related to the change of the historical sensing data. In some embodiments, the sensing statistical data may include a differential pressure statistic and a temperature difference statistic. A set of sensing statistical data includes the differential pressure statistic and the temperature difference statistic corresponding to the historical sensing data of the set.

[0051] In some embodiments, the processor can divide the historical sensing data into intervals to determine multiple sets of historical sensing data, and for each set of historical sensing data, calculate the sensing statistical data of the set. See the specific description below for this part of the content.

[0052] The differential pressure statistic refers to the statistical data related to the change of the gas pressure. In some embodiments, the differential pressure statistic may include the extreme value of the pressure change rate, the median value of the pressure change rate, etc.

[0053] Wherein, the pressure change rate refers to the rate of change of the pressure. In some embodiments, the pressure change rate can be represented by the ratio of the pressure change amount to the time corresponding to the pressure change amount, the extreme value of the pressure change rate can include the maximum value and / or the minimum value of multiple pressure change rates, and the median value of the pressure change rate can refer to the median of multiple pressure change rates.

[0054] The temperature difference statistic refers to the statistical data related to the change of the gas temperature and / or the equipment temperature. In some embodiments, the temperature difference statistic may include the extreme value of the gas temperature change rate, the median value of the gas temperature change rate, the extreme value of the equipment temperature change rate, the median value of the equipment temperature change rate, etc.

[0055] Among them, the temperature change rate refers to the rate of change of temperature. The gas temperature change rate and the equipment temperature change rate respectively refer to the temperature change rate of the gas temperature and the temperature change rate of the equipment temperature. In some embodiments, the temperature change rate can be represented by the ratio of the temperature change amount to the time corresponding to the temperature change amount. The extreme value of the temperature change rate can include the maximum value and / or the minimum value of multiple temperature change rates. The median value of the temperature change rate can refer to the median of multiple temperature change rates. The temperature change amount can be positive or negative, and the corresponding temperature change rate can be positive or negative. When the temperature change rate is positive, it indicates that the equipment temperature and / or the gas temperature is increasing; when the temperature change rate is negative, it indicates that the equipment temperature and / or the gas temperature is decreasing. When the equipment temperature and / or the gas temperature is decreasing, the cold energy recovery equipment does not work.

[0056] In some embodiments, the processor can determine multiple sets of sensing statistical data based on historical sensing data in various ways.

[0057] For example, the processor can determine multiple sets of sensing statistical data through statistical analysis based on historical sensing data. Specifically, it can include the following steps S11 - step S13: Step S11, divide the range of the historical first pressure into intervals, and divide the historical sensing data into multiple groups.

[0058] In some embodiments, the processor can divide the multiple historical first pressures at multiple historical moments into different intervals according to a preset pressure step, and divide the multiple historical sensing data corresponding to each historical first pressure interval into a group. Among them, the preset pressure step can be default set by the processor or preset by the technician based on experience.

[0059] For example, if the range of the historical first pressure is 1MPa to 5MPa and the preset pressure step can be 1MPa, then the historical first pressure can be divided into multiple intervals such as [1MPa, 2MPa), [2MPa, 3MPa), [3MPa, 4MPa), [4MPa, 5MPa], and the historical sensing data corresponding to each historical first pressure interval are respectively divided into groups.

[0060] Step S12, for each group of historical sensing data, calculate multiple historical pressure change rates within the group, divide the multiple historical pressure change rates within the group into intervals, and divide a group of historical sensing data into multiple sets.

[0061] In some embodiments, for each group of historical sensing data, two adjacent historical moments form a historical time period. Each group of historical sensing data can include multiple historical time periods, and then multiple historical pressure change rates can be calculated correspondingly. For a historical time period, the processor can calculate a corresponding historical pressure change rate based on the historical first pressure at the start historical moment and the historical second pressure at the end historical moment. For example, referring to Table (1), The historical first pressure at a historical moment is , The historical second pressure at a historical moment is , then The historical pressure change amount in a historical time period is , and the corresponding historical pressure speed change is .

[0062] In some embodiments, the processor may divide multiple corresponding historical pressure speed changes in each group of historical sensing data into different intervals according to a preset speed change step, and divide the multiple historical sensing data corresponding to each historical pressure speed change interval into a group. Among them, the preset speed change step can be default set by the processor or preset by a technician based on experience.

[0063] For example, for a group of historical sensing data with a historical first pressure interval of [1 MPa, 2 MPa), the corresponding historical pressure speed change range is [0.1 MPa / s, 0.4 MPa / s], and the preset speed change step can be 0.1 MPa / s. Then the historical pressure speed change can be divided into multiple intervals such as [0.1 MPa / s, 0.2 MPa / s), [0.2 MPa / s, 0.3 MPa / s), [0.3 MPa / s, 0.4 MPa / s), and the historical sensing data corresponding to each historical pressure speed change interval are respectively divided into a group, that is, this group of historical sensing data with a historical first pressure interval of [1 MPa, 2 MPa) is subdivided into 3 groups according to the historical pressure speed change.

[0064] Only as an example, in combination with the following table (2), for a group of historical sensing data corresponding to a historical first pressure interval , the corresponding historical pressure speed change range is . According to the preset speed change step, the historical pressure speed change is divided into , 2 intervals, and the historical sensing data corresponding to the 2 historical pressure speed change intervals are respectively divided into 2 groups, that is, this group of historical sensing data with a historical first pressure interval is subdivided into 2 groups according to the historical pressure speed change.

[0065] Among them, dividing a group of historical sensing data into multiple groups can be represented by the following table (2). The multiple historical sensing data corresponding to each historical first pressure interval are divided into a group, and the multiple historical sensing data corresponding to each historical pressure speed change interval within each group are divided into a group.

[0066] Table (2)

[0067] Among them, , , …, are the minimum values of the historical first pressures in different historical first pressure intervals respectively, , …, are the maximum values of the historical first pressures in different historical first pressure intervals respectively, represents the th historical first pressure interval, , …, are respectively , …, the historical first pressures corresponding to the historical moments, , …, are respectively , …, the historical second pressures corresponding to the historical moments; , …, are respectively , …, the historical pressure speed changes in the historical time periods, represents the historical pressure speed change interval corresponding to the first group of historical sensing data of the first family, represents the th family and the th group of historical sensing data corresponding to the historical pressure speed change interval.

[0068] Step S13. For each group of historical sensing data, calculate the sensing statistical data of this group.

[0069] In some embodiments, for each group of historical sensing data, the processor may determine multiple historical pressure speed changes, historical gas temperature speed changes, and historical equipment temperature speed changes corresponding to this group of historical sensing data. The calculation processes for the historical gas temperature speed change and the historical equipment temperature speed change can refer to the calculation process of the historical pressure speed change in step S12, which will not be elaborated here.

[0070] In some embodiments, the processor may determine the median and extreme values of multiple historical pressure speed changes, the median and extreme values of multiple historical gas temperature speed changes, and the median and extreme values of multiple historical equipment temperature speed changes through statistical analysis, so as to determine a set of sensing statistical data of this group of historical sensing data.

[0071] In some embodiments, the processor may also determine sensing speed data based on historical sensing data; group the sensing speed data based on the historical sensing data or the sensing speed data to determine one or more groups of sensing speed data; and perform intra-group grouping on each group of sensing speed data to determine multiple groups of sensing statistical data. For more details on this part, please refer to Figure 3 its related description.

[0072] Step S230: Determine an adjustment instruction based on the current sensing data and the sensing statistical data.

[0073] The current sensing data refers to the sensing data in the current time period. Here, the current time period refers to a relatively short time period before the current moment. For example, 5 minutes or 10 minutes before the current moment. The current sensing data may include the sensing data at two or more time points within the current time period.

[0074] In some embodiments, the current sensing data may be monitored and obtained by the sensing device and uploaded to the gas company management platform through the gas company sensing network platform, and the processor may directly retrieve the current sensing data.

[0075] The adjustment instruction refers to an instruction for adjusting the relevant device parameters within the gas equipment object platform.

[0076] In some embodiments, the adjustment instruction may be sent by the gas company management platform to the government safety supervision and management platform and configured to adjust the cooling parameters of the cold energy recovery equipment and the pressure regulation parameters of the pressure regulation equipment.

[0077] In some embodiments, the adjustment instruction may include target cooling parameters and target pressure regulation parameters.

[0078] The cooling parameters refer to the working parameters of the cold energy recovery equipment, which may include the cooling working power of the cold energy recovery equipment and the refrigerant circulation speed, etc.

[0079] The refrigerant circulation speed refers to the speed at which the refrigerant passes through the circulation system and can be represented by the volume or mass of the fluid passing through the circulation system per unit time.

[0080] The target cooling parameters refer to the cooling parameters that the cold energy recovery equipment needs to execute, including the target cooling working power and the target refrigerant circulation speed, etc.

[0081] The pressure regulation parameters refer to the working parameters of the pressure regulation equipment, which may include the pressure regulation working power of the pressure regulation equipment and the opening of the pressure valve.

[0082] The pressure regulation working power refers to the working power when the pressure regulation equipment adjusts the pressure of the gas.

[0083] The opening degree of the pressure valve refers to the opening degree of the pressure regulating valve in the pressure regulating equipment. The larger the opening degree of the pressure valve, the greater the opening degree of the pressure regulating valve, the larger the passage for the gas to pass through the valve, and the greater the degree of reduction of the gas pressure.

[0084] The target pressure regulating parameter refers to the pressure regulating parameter that the pressure regulating equipment needs to execute, including the target pressure regulating working power, the target opening degree of the pressure valve, etc.

[0085] In some embodiments, the processor can determine an adjustment instruction based on the current sensing data and the sensing statistical data. Specifically, it may include the following steps S21 - S25: Step S21, determine the current pressure change rate and the current temperature change rate based on the current sensing data.

[0086] In some embodiments, the processor can calculate the current pressure change rate and the current temperature change rate of the current time period based on the current sensing data at two time points within the current time period. The calculation methods of the current pressure change rate and the current temperature change rate are similar to those of the historical pressure change rate and the historical temperature change rate. Specifically, reference can be made to the relevant descriptions in steps S12 and S13 above, which will not be elaborated here.

[0087] Step S22, determine the current statistical data based on the current first pressure, the current pressure change rate, and the historical sensing data.

[0088] The current statistical data refers to the statistical data of the current sensing data. In some embodiments, the processor can judge the historical first pressure range to which the current first pressure belongs, determine a group of historical sensing data corresponding thereto; in this group of historical sensing data, judge the historical pressure change rate range to which the current pressure change rate belongs, determine a set of historical sensing data corresponding thereto; and determine the sensing statistical data corresponding to this set of historical sensing data as the current statistical data.

[0089] Step S23, compare the current temperature change rate with the current statistical data. In response to meeting the preset conditions, determine the target cooling parameter based on the current temperature change rate. The preset conditions are that the current gas temperature change rate is greater than the median of the gas temperature change rate in the current statistical data and / or the current equipment temperature change rate is greater than the median of the equipment temperature change rate in the current statistical data.

[0090] It can be understood that when the current temperature change rate exceeds the median of the historical temperature change rate, it indicates that the current temperature rises too fast, and it is necessary to cool down the pressure regulating equipment by adjusting the cooling parameters of the cold energy recovery equipment.

[0091] In some embodiments, in response to the current temperature change rate satisfying a preset condition, the processor may determine a target cooling parameter based on the current temperature change rate by querying a first preset comparison table. The first preset comparison table may include the correspondence between the temperature change rate and the cooling parameter. The first preset comparison table may be constructed by a technician based on historical data or experience.

[0092] In some embodiments, the processor may determine the temperature change rate in the first preset comparison table that has the highest similarity to the current temperature change rate, and determine the corresponding cooling parameter as the target cooling parameter. The similarity may be represented by the absolute value of the difference between the current temperature change rate and the temperature change rate in the table. The smaller the absolute value of the difference, the higher the similarity. In some embodiments, the processor may preferentially adjust the cooling parameter of the cold energy recovery device based on the difference between the current cooling parameter and the target cooling parameter.

[0093] Step S24: In response to the temperature change rate satisfying the preset condition after cooling based on the target cooling parameter for a preset time period, determine a target pressure regulation parameter based on the device temperature after the preset time period.

[0094] The preset time period refers to a period of time after the current moment during which the cold energy recovery device operates based on the target cooling parameter. The preset time period may be set by a technician according to historical experience. In some embodiments, the processor may determine the pressure regulation working efficiency by querying a second preset comparison table based on the device temperature after the preset time period; and determine the target pressure regulation parameter based on the current pressure regulation parameter and the pressure regulation working efficiency. The second preset comparison table may include the correspondence between the device temperature and the pressure regulation working efficiency. The pressure regulation working efficiency refers to the efficiency of the pressure regulation device in regulating the gas pressure, and may be represented by the ratio of the actual working ability of the pressure regulation device to the theoretical working ability. For example, a pressure regulation working efficiency of 80% represents that the ratio of the output power to the input power of the pressure regulation device is 80%. In some embodiments, the pressure regulation working efficiency is negatively correlated with the device temperature.

[0095] For example, the processor may look up the pressure regulation working efficiency corresponding to the device temperature after the preset time period in the second preset comparison table, and determine the ratio of the current pressure regulation working power to the pressure regulation working efficiency as the target pressure regulation working power.

[0096] Step S25: Determine a pressure regulation instruction based on the target cooling parameter and the target pressure regulation parameter.

[0097] In some embodiments, the processor may input the target cooling parameter and the target pressure regulation parameter into a preset template of the pressure regulation instruction to generate a pressure regulation instruction.

[0098] In some embodiments of the present specification, by statistically analyzing historical sensing data, sensing statistical data can be determined, and then the variation laws of gas pressure, equipment temperature, and gas temperature can be evaluated; by grouping the sensing statistical data, the gas can be divided into different intervals for reference, facilitating different levels of pressure regulation operations and cooling operations on gases with different pressures, so that the pressure regulation equipment and cold energy recovery equipment can maintain good working efficiency.

[0099] Figure 3 is an exemplary flowchart for determining multiple groups of sensing statistical data as shown in some embodiments of the present specification. As Figure 3 shown, process 300 may include the following steps. In some embodiments, process 300 may be executed by the gas company management platform 131. For example, executed by a processor.

[0100] Step S310, based on historical sensing data, determine sensing speed data.

[0101] Sensing speed data refers to data related to the change speed of historical sensing data. The sensing speed data may include pressure variation speed and temperature variation speed for multiple historical time periods.

[0102] The historical time period refers to the time period formed by two adjacent historical moments. For more information about historical sensing data, historical moments, pressure variation speed, and temperature variation speed, reference can be made to the relevant descriptions above.

[0103] In some embodiments, the processor may determine the pressure variation speed and temperature variation speed for multiple historical time periods based on historical sensing data, that is, determine the sensing speed data. For more information about determining the pressure variation speed and temperature variation speed for multiple historical time periods based on historical sensing data, reference can be made to the relevant descriptions in step S12 and step S13.

[0104] Step S320, based on historical sensing data or sensing speed data, classify the sensing speed data to determine one or more groups of sensing speed data.

[0105] Exemplarily, referring to Table (2), one or more groups of sensing speed data can be represented by the following Table (3), and the multiple sensing speed data corresponding to each historical first pressure interval are classified into one group.

[0106] Table (3)

[0107] Wherein, 、 、…、 are respectively 、 、…、 the historical gas temperature variation speed of the historical time period, , ,…, are respectively , ,…, the historical gas temperatures corresponding to historical moments; , ,…, are respectively , ,…, the historical equipment temperature variable speeds of historical time periods, , ,…, are respectively , ,…, the historical equipment temperatures corresponding to historical moments. For the explanations of other parameters, please refer to Table (1) and Table (2).

[0108] One or more groups of sensing speed data refer to the sensing speed data respectively corresponding to each group of historical sensing data after being grouped. One group of historical sensing data corresponds to one group of sensing speed data.

[0109] In some embodiments, the processor may group the sensing speed data based on the historical first pressure in the historical sensing data to determine one or more groups of sensing speed data. For more content on this part, please refer to Steps S11 - S13 and their related descriptions.

[0110] In some embodiments, the processor may determine clustering features based on the sensing speed data, cluster the clustering features, and determine one or more groups of sensing speed data.

[0111] Among them, the clustering feature refers to the basis feature for clustering the sensing speed data. One sensing speed data in one historical time period corresponds to one clustering feature. Taking the sensing speed data of the historical time period as an example, the processor may determine its corresponding clustering feature through the following formula (1): (1) wherein, , , respectively refer to the historical pressure variable speed, historical gas temperature variable speed, and historical equipment temperature variable speed of the historical time period, represents the clustering feature of.

[0112] In some embodiments, the processor may cluster multiple clustering features, determine the sensing speed data corresponding to the clustering features of the same class in the clustering result as a group of sensing speed data, and the sensing speed data corresponding to multiple classes of clustering features are respectively multiple groups of sensing speed data. Among them, the clustering method includes but is not limited to mean shift clustering, etc.

[0113] Step S330: Perform intra-group grouping on each group of sensing speed data to determine multiple groups of sensing statistical data.

[0114] In some embodiments, the processor may divide multiple historical pressure variable speeds within each group of sensing speed data into intervals, divide a group of sensing speed data into multiple groups, and then calculate the sensing statistical data of each group. For the specific process of this part, reference can be made to the relevant content of steps S12 - S13.

[0115] Exemplarily, referring to Table (2) and Table (3), the multiple groups of sensing statistical data corresponding to the intra-group grouping of one or more groups of sensing speed data can be represented by the following Table (4): Table (4)

[0116] Among them, represents the pressure difference statistic corresponding to the th group of sensing speed data in the th group, respectively represent the maximum value, minimum value, and median value of the historical pressure variable speed, respectively represent the gas temperature statistic and equipment temperature statistic corresponding to the th group of sensing speed data in the th group, respectively represent the maximum value, minimum value, and median value of the historical gas temperature variable speed, respectively represent the maximum value, minimum value, and median value of the historical equipment temperature variable speed. For the explanations of the remaining parameters, reference can be made to Table (1), Table (2), and Table (3).

[0117] In some embodiments, for each group of sensing speed data, the processor may perform intra-group grouping on the sensing speed data based on the historical adjustment time points of the pressure regulating parameter and the cooling parameter, determine multiple groups of sensing speed data; and determine multiple groups of sensing statistical data based on the multiple groups of sensing speed data.

[0118] For more content about the pressure regulating parameter and the cooling parameter, reference can be made to the relevant descriptions above.

[0119] The historical adjustment time point refers to the historical time point when the pressure regulating parameter and / or the cooling parameter changes. In some embodiments, the historical adjustment time point is the historical time point within the acquisition period corresponding to the historical sensing data.

[0120] In some embodiments, the historical adjustment time points can be obtained by a processor or a technician based on historical data. For example, the historical sensing data includes sensing data from a historical moment to a historical moment , then the acquisition period of the historical sensing data is , and within the acquisition period, and at the historical moments, the voltage regulation parameters change, and within the acquisition period, and at the historical moments, the cooling parameters change, then the historical adjustment time points include , , and .

[0121] In some embodiments, for a family of sensing speed data, the processor can group the family of sensing data based on the historical adjustment time points within the acquisition period corresponding to the family of sensing speed data to determine multiple groups of sensing speed data.

[0122] For example, in combination with Table (3), the acquisition period corresponding to the first family of sensing speed data is , and within the period, the voltage regulation parameter changes at the moment, and the cooling parameter changes at the moment. Then, according to the historical adjustment time points , , the first family of sensing speed data is divided into , , 3 groups of sensing speed data corresponding to these 3 time periods.

[0123] In some embodiments, the processor determines multiple fluctuation results corresponding to multiple groups of sensing speed data; based on the multiple fluctuation results, determines multiple confidence levels of the multiple groups of sensing speed data; and based on the multiple confidence levels, determines the screened multiple groups of sensing statistical data. The specific calculation process can refer to the relevant content of step S13.

[0124] In some embodiments of the present specification, based on the historical adjustment time points of the voltage regulation parameters and the cooling parameters, the reasonable division of the intra-family groups of the sensing speed data is further carried out, so that the statistical results of the sensing speed data are more accurate.

[0125] In some embodiments, the processor can determine multiple fluctuation results corresponding to multiple groups of sensing speed data; based on the multiple fluctuation results, determine multiple confidence levels of the multiple groups of sensing speed data; and based on the multiple confidence levels, determine the screened multiple groups of sensing statistical data.

[0126] The fluctuation result refers to the data that can characterize the fluctuation of a set of sensing speed data. In some embodiments, a set of sensing speed data corresponds to one fluctuation result, and the fluctuation result of a set of sensing speed data can be represented by the variance or standard deviation of the set of sensing speed data.

[0127] Exemplarily, in combination with Table (4), if, after within-family grouping, it is determined that among the first-family sensing speed data, the sensing speed data in a time period is divided into Group 1 of Family 1, then this set of sensing speed data includes , , , , , . Calculate the standard deviations of the historical pressure speed change , , the standard deviations of the historical gas temperature speed change , , the standard deviations of the historical equipment temperature speed change , . Perform a weighted sum of the 3 standard deviations, and use the result of the weighted sum as the fluctuation result of this set of sensing speed data.

[0128] Confidence refers to a measure that can characterize the reliability of data. In some embodiments, confidence can be represented by a numerical value.

[0129] In some embodiments, confidence can have a negative correlation with the fluctuation result. The larger the fluctuation result, the lower the confidence. For example, the processor can determine the reciprocal of the fluctuation result of a set of sensing speed data as the confidence of this set of sensing speed data.

[0130] In some embodiments, for the sensing speed data of each family, the processor can screen out one or more sets of sensing speed data with a confidence less than a preset confidence threshold within the family, and determine the screened multiple sets of sensing statistical data based on one or more sets of sensing speed data with a confidence not less than the preset confidence threshold within each family. Among them, the process of determining the sensing statistical data based on the sensing speed data can refer to the relevant content of step S13.

[0131] In some embodiments, the preset confidence threshold can be a fixed value default set by the processor or set by a technician based on experience, or can be determined by the processor based on confidence. For example, for a family of sensing speed data, the processor can determine the average value of the confidence of all sets of sensing speed data in this family as the preset confidence threshold of this family.

[0132] In some embodiments of the present specification, by determining the fluctuation result corresponding to the sensing speed data to reflect the fluctuation of the sensing speed data, determining the confidence level based on the fluctuation result, and further screening the sensing speed data based on the confidence level, it helps to exclude abnormal or unreliable data caused by accidental factors such as equipment failures and environmental interferences, and retain the relatively reliable sensing statistical data, thereby improving the accuracy of subsequent analysis and decision-making.

[0133] In some embodiments of the present specification, the sensing speed data is determined based on historical sensing data, and then the sensing speed data is grouped into families and sub-grouped within the families based on the historical sensing data or the sensing speed data, so as to accurately determine the multiple sets of sensing statistical data of the multiple sets of sensing speed data, which can reflect the statistical conditions of the historical sensing data of each group and is conducive to the accurate determination of the adjusted instructions.

[0134] Figure 4 is an exemplary flowchart for determining the pressure regulation parameters at the first moment as shown in some embodiments of the present specification. As Figure 4 shown, process 400 includes the following steps. In some embodiments, process 400 can be executed by the gas company management platform 131. For example, executed by a processor.

[0135] In some embodiments, the gas equipment object platform further includes multiple input pipelines of the gas pressure regulating station, the pressure regulating equipment includes distribution equipment, pressure boosting equipment, and / or pressure reducing equipment, and the pressure regulation parameters include the distribution parameters of the distribution equipment, the pressure boosting parameters of the pressure boosting equipment, and / or the pressure reducing parameters of the pressure reducing equipment; the processor can determine the initial pressure regulation parameters based on the multiple first pressures corresponding to the multiple input pipelines and the target output pressure, and control the pressure regulating equipment to operate based on the initial pressure regulation parameters; obtain the sensing data after the first time interval; and determine the pressure regulation parameters at the first moment based on the sensing data.

[0136] For more content about the above equipment, pressure regulation parameters, first pressure, and sensing data, reference can be made to the relevant descriptions above.

[0137] The distribution parameter refers to the working parameter for the distribution equipment to control the gas flow direction. For example, mixing the gas in input pipeline L1 and input pipeline L2 and then distributing it to the pressure boosting equipment. Another example is to distribute input pipeline L1 to the pressure reducing equipment and input pipeline L2 to the pressure boosting equipment. Another example is that when the gas pressures in multiple input pipelines differ too much to directly mix the gas, input pipeline L1 can be distributed to the pressure reducing equipment and input pipeline L2 to the pressure boosting equipment, respectively reduce and increase the pressure and then mix the gas, and the mixed gas is re-introduced into the pressure boosting equipment through a pipeline with multi-stage cyclic pressure boosting.

[0138] The boost parameters refer to the operating parameters of the boosting equipment. For example, compressor power, motor speed, boost valve opening, boost inlet flow rate, etc. Among them, the boosting equipment may include multiple compressors, and different compressors are used to meet different boosting requirements. Compressor power refers to the operating power of the compressor. The greater the compressor power, the faster the boosting speed of the boosting equipment. The faster the motor speed, the faster the boosting speed of the boosting equipment. The boost valve opening refers to the opening degree of the pressure regulating valve in the boosting equipment. The greater the boost valve opening, the greater the opening degree of the pressure regulating valve, and the faster the boosting speed. The boost inlet flow rate refers to the flow rate of the gas entering the boosting equipment. The faster the boost inlet flow rate, the slower the boosting speed of the boosting equipment.

[0139] The pressure reduction parameters refer to the operating parameters of the pressure reduction equipment. For example, the pressure reducing valve opening, the pressure reduction inlet flow rate, etc. Among them, the pressure reduction inlet flow rate refers to the flow rate of the gas entering the pressure reduction equipment. The faster the pressure reduction inlet flow rate, the slower the pressure reduction speed of the pressure reduction equipment. The pressure reducing valve opening refers to the opening degree of the pressure regulating valve in the pressure reduction equipment. The greater the pressure reducing valve opening, the greater the opening degree of the pressure regulating valve, and the faster the pressure reduction speed.

[0140] Step S410: Based on the multiple first pressures corresponding to the multiple input pipelines and the target output pressure, determine the initial pressure regulation parameters, and control the pressure regulation equipment to operate based on the initial pressure regulation parameters.

[0141] The target output pressure refers to the expected value of the gas pressure after the gas passes through the pressure regulation equipment.

[0142] In some embodiments, the target output pressure can be preset by a technician based on experience.

[0143] In some embodiments, the target output pressure can also be determined by the processor based on the gas demand of the output pipeline. For the content of this part, please refer to the relevant description below.

[0144] In some embodiments, one input pipeline corresponds to a first pressure. The first pressure corresponding to each input pipeline can refer to the gas pressure of each input pipeline before the gas is regulated by the pressure regulation equipment. For the description of obtaining the first pressure, please refer to the relevant description of step S210.

[0145] The initial pressure regulation parameters refer to the initial values of the pressure regulation parameters when the system is started or adjusted. In some embodiments, the initial pressure regulation parameters may include initial distribution parameters, initial boost parameters, and initial pressure reduction parameters. For example, the initial pressure regulation parameters can be expressed as: [(input pipeline L1 is assigned to the pressure reduction equipment, input pipeline L2 is assigned to the boosting equipment), (compressor power P1 of compressor 1, motor speed R, boost valve opening K1, boost inlet flow rate E1), (pressure reducing valve opening K2, pressure reduction inlet flow rate E2)].

[0146] In some embodiments, the processor may determine an initial allocation parameter based on a plurality of first pressures and a target gas pressure according to a first preset rule; and determine an initial pressure increase parameter and an initial pressure decrease parameter based on the initial allocation parameter through a first preset algorithm.

[0147] Among them, the first preset rule and the first preset algorithm may be preset by technicians. For example, the first preset rule may be that when the first pressure of the input pipeline is greater than the target gas pressure, the gas of the input pipeline is allocated to the pressure reduction device; when the first pressure of the input pipeline is less than the target gas pressure, the gas of the input pipeline is allocated to the pressure increase device. The first preset algorithm may be to determine the compression ratio as the ratio of the first pressure of the input pipeline allocated to the pressure increase device to the target gas pressure, calculate the compressor power according to the compression ratio, or determine the opening of the pressure reducing valve based on the first pressure of the input pipeline allocated to the pressure reduction device and the target gas pressure. Among them, the opening of the pressure reducing valve is positively correlated with the difference between the first pressure and the target gas pressure, and the greater the difference between the first pressure and the target gas pressure, the greater the opening of the pressure reducing valve.

[0148] In some embodiments, the processor may control the operation of the pressure regulating device according to the initial pressure regulating parameter.

[0149] Step S420: Obtain the sensing data after the first time interval.

[0150] The first time interval refers to the time interval used to determine whether the parameter needs to be adjusted greatly.

[0151] In some embodiments, the first time interval may be related to the difference between the first pressure and the target output pressure, and the greater the difference between the first pressure and the target output pressure, the longer the first time interval.

[0152] In some embodiments, the first time intervals corresponding to different gas pressure regulating stations may be the same or different, and the first time interval is related to the number of input pipelines of the gas pressure regulating station.

[0153] In some embodiments, the processor may determine the first time interval by querying a first preset table. The first preset table includes the correspondence between the number of input pipelines of the gas pressure regulating station and the first time interval. For example, the greater the number of input pipelines of the gas pressure regulating station, the longer the first time interval. The first preset table may be constructed by technicians based on historical data and prior experience.

[0154] In some embodiments of the present specification, adjusting the first time interval according to the number of input pipelines can flexibly adapt to gas pressure regulating stations of different scales and complexities, and significantly improve the overall efficiency and stability of the gas pressure regulating system.

[0155] In some embodiments, the first time interval may also be related to a second time interval, and the first time interval is longer than the second time interval.

[0156] The second time interval refers to the time interval used to determine whether a parameter requires a minor adjustment.

[0157] In some embodiments, based on the sensing data after the first time interval, it can be determined whether the pressure regulating parameter and the cooling parameter need to be adjusted, and based on the sensing data after the second time interval, it can be determined whether the refrigerant circulation speed in the cooling parameter needs to be adjusted. The first time interval is longer than the second time interval.

[0158] There is no sequential relationship between the first time interval and the second time interval. For example, the first time interval can be before the second time interval. Another example is that the second time interval can be included within the first time interval.

[0159] In some embodiments, the second time interval can be determined based on querying a second preset table. For the content of this part, reference can be made to the relevant description below.

[0160] In some embodiments of this specification, only the refrigerant circulation speed is adjusted based on the second time interval, and the pressure regulating parameter and the cooling parameter are adjusted as a whole based on the first time interval. Therefore, when determining the first time interval, the second time interval is considered, and the first time interval is set to be longer than the second time interval, making the first time interval more reasonable and reliable.

[0161] In some embodiments, after the first time interval has passed from the current moment, the sensing device monitors and obtains the sensing data, and uploads it to the gas company management platform through the gas company sensing network platform. The processor can directly retrieve the sensing data after the first time interval.

[0162] Step S430, based on the sensing data, determine the pressure regulating parameter at the first moment.

[0163] The first moment refers to the time point after the first time interval has passed from the current moment.

[0164] In some embodiments, the processor can determine the pressure regulating parameter at the first moment based on the sensing data after the first time interval. For the process of determining the pressure regulating parameter based on the sensing data, reference can be made to the relevant description in step S230. The process is similar and will not be elaborated here.

[0165] In some embodiments of this specification, the gas pressure regulating station incorporates multiple devices into the Internet of Things, enabling precise joint control of the pressure regulating device and the cold energy recovery device. The gas company management platform determines the initial pressure regulating parameter based on the first pressure of multiple input pipelines and the target output pressure, and by obtaining and analyzing the sensing data after the first time interval, it can continuously judge the system changes, and thus determine the pressure regulating parameter again to ensure that the pressure regulating parameter meets the real-time requirements, improving the stability and efficiency of the system operation.

[0166] In some embodiments, the cold energy recovery device may further include an expansion refrigeration device, which is connected to the pressure reduction device; the cooling parameter further includes the opening degree of the refrigerant valve of the expansion refrigeration device; the gas company management platform is further configured to: based on the sensing data and the initial pressure regulation parameter after the first time interval, determine the cooling parameter at the first moment. For more content about the initial pressure regulation parameter, reference can be made to the relevant description in step S410.

[0167] For more content about the cooling parameter, reference can be made to the relevant description above.

[0168] The opening degree of the refrigerant valve refers to the opening degree of the valve that controls the refrigerant flow rate. The opening degree of the refrigerant valve can be expressed as a percentage. For example, the opening degrees of the refrigerant valve being 100% and 50% respectively indicate that the valve controlling the refrigerant flow rate is fully opened and half-opened. The larger the opening degree of the refrigerant valve, the faster the refrigeration speed of the expansion refrigeration device.

[0169] In some embodiments, the processor may, based on the sensing data and the initial pressure regulation parameter after the first time interval, determine the cooling parameter at the first moment through a second preset algorithm. The second preset algorithm can be pre-set by those skilled in the art. For example, the second preset algorithm is to calculate the temperature reduction value through a mathematical model such as the ideal gas equation based on the pressure reduction inlet flow rate of the pressure reduction device, the first pressure of the input pipeline allocated to the pressure reduction device, and the gas temperature. The temperature reduction value is positively correlated with the opening degree of the refrigerant valve. The temperature reduction value refers to the amount of temperature drop of the gas after passing through the gas pressure regulating station. The larger the temperature reduction value, the more cold energy released by the gas pressure reduction, and thus a larger refrigerant flow rate is required to absorb the cold energy, so the opening degree of the refrigerant valve should also be larger.

[0170] In some embodiments of this specification, the expansion refrigeration device can utilize the cold energy released by pressure reduction for refrigeration. By directly connecting the expansion refrigeration device to the pressure reduction device, energy conservation can be achieved; the cooling parameter includes the opening degree of the refrigerant valve, which can accurately regulate the refrigerant flow rate to control the cooling operation; by analyzing the sensing data and the initial pressure regulation parameter after the first time interval, the cooling parameter at the first moment can be accurately determined, which is beneficial to timely and accurately adjust the parameters of the cold energy recovery device.

[0171] In some embodiments, the processor may determine the target output pressure based on the gas demand of the output pipeline; based on the multiple first pressures corresponding to the multiple input pipelines, the target output pressure, and the gas temperature, determine the pressure regulation parameter and the cooling parameter for the future period through a pressure difference model, and the pressure difference model is a machine learning model.

[0172] The gas demand refers to the data related to the gas demand of the downstream users connected to the output pipeline. For example, the gas transmission pressures of the downstream users at each time period.

[0173] Among them, the gas transmission pressure refers to the input pressure of the gas when downstream users use the gas. Downstream users refer to the users to which the gas in the output pipeline flows. For example, residential buildings, office buildings, etc. Each time period can be divided in various forms. For example, every 24 hours of each day in each season.

[0174] In some embodiments, the processor can directly read the gas demand uploaded by the gas device object platform.

[0175] In some embodiments, the target output pressure can include the target output pressures at multiple future times. For example, if the current time is 12:00 on September 20, 2024, the target output pressure can include the target output pressures at multiple future times such as 13:00, 14:00, 15:00 on September 20, 2024. Correspondingly, the target output pressure can be expressed in a sequence form. For example, the target output pressure can be expressed as … , where 、 、…、 represent the 1st, 2nd, …, th future times, 、 、…、 represent the target output pressures corresponding to the 1st, 2nd, …, th future times.

[0176] In some embodiments, the processor can determine the target output pressure at a future time through statistical analysis based on the gas demand in the output pipeline. For example, the processor can, based on the gas demand in the output pipeline, statistically analyze the gas transmission pressure during the period from 14:00 to 15:00 every day in the autumn of 2023 (divided meteorologically from August 23 to November 20), and determine the average value of the multiple gas transmission pressures as the target output pressure at 15:00 on September 20, 2024 at this future time.

[0177] The differential pressure model refers to a model used to determine the pressure regulating parameters and cooling parameters for a future period. In some embodiments, the differential pressure model can be a machine learning model. For example, a Recurrent Neural Network (RNN), etc.

[0178] Figure 5 is an exemplary schematic diagram of the differential pressure model shown in some embodiments of this specification.

[0179] In some embodiments, such as Figure 5As shown, the inputs of the differential pressure model 520 include one or more first pressures 511 corresponding to one or more input pipelines at the current moment, the target output pressure 512, the gas temperature 513, and the equipment temperature 514, and the outputs are the pressure regulation parameters 531 and the cooling parameters 532 in the future time period.

[0180] Among them, the pressure regulation parameters 531 include the pressure regulation working power 531-1 and the pressure valve opening 531-2, and the cooling parameters 532 include the cooling working power 532-1 and the refrigerant circulation speed 532-2.

[0181] For example, the current moment is , and the target output pressure is … , and the pressure regulation parameters and cooling parameters in the future time period output by the differential pressure model can be 、 . Among them, represents the th future moment, represents the th future time period, ( , )represents the pressure regulation parameters of the th future time period, and respectively represent the pressure regulation working power and the pressure valve opening of the th future time period; ( , )represents the cooling parameters of the th future time period, and respectively represent the cooling working power and the refrigerant circulation speed of the th future time period.

[0182] For more content about the above-mentioned multiple parameters (such as the first pressure, the target output pressure, the refrigerant circulation speed, etc.), reference can be made to the relevant descriptions above.

[0183] In some embodiments, the differential pressure model can be obtained by training with multiple training samples. A training sample includes multiple sample first pressures, sample gas temperatures, sample equipment temperatures, and sample target output pressures corresponding to multiple input pipelines at the sample moment, and the training label corresponding to the training sample is the actual pressure regulation parameters and the actual cooling parameters in the sample time period. The sample time period is a time period composed of multiple future moments at the sample moment.

[0184] In some embodiments, the training samples and training labels can be screened and obtained from historical data by a processor according to a second preset rule. The second preset rule can be pre-set by a technician. For example, the second preset rule can include the following steps S31 - step S32: Step S31, obtain a plurality of candidate training samples and corresponding a plurality of candidate training labels based on the historical data.

[0185] In some embodiments, each candidate training sample includes the first pressure, the second pressure, the gas temperature, and the equipment temperature at two candidate moments, and the corresponding candidate training label is the pressure regulating parameter and the cooling parameter for the candidate time period.

[0186] Among them, the candidate moment can be a historical moment, and the candidate time period is the time period with two candidate moments as the head and the tail. Within the candidate time period formed by two candidate moments, the pressure regulating parameter and the cooling parameter remain unchanged, that is, the historical adjustment time point is not included in the candidate time period. For the content about the historical adjustment time point, reference can be made to the relevant description above.

[0187] Step S32, determine the efficiency score of the candidate training sample, and select the candidate training sample with an efficiency score higher than the preset score threshold as the training sample.

[0188] Among them, the efficiency score refers to the ratio of the energy consumption to the pressure change amount. The energy consumption refers to the sum of the energy consumption of the pressure regulating device and the cold energy recovery device, and the energy consumption can include the device power consumption and the refrigerant supplement amount, etc. In some embodiments, the energy consumption can be obtained based on the monitoring data of relevant monitoring devices. The relevant monitoring devices can include an electric meter, a refrigerant addition monitoring device, etc.

[0189] The pressure change amount refers to the difference obtained by subtracting the first pressure at the start candidate moment from the second pressure at the end candidate moment in the candidate time period corresponding to the candidate training sample.

[0190] The preset score threshold can be set by the processor or by a technician based on experience.

[0191] In some embodiments, the processor can train the differential pressure model based on a plurality of training samples with training labels.

[0192] In some embodiments, the processor can input the training sample into the initial differential pressure model, construct a loss function based on the output result of the initial differential pressure model and the training label, and iteratively update the initial differential pressure model based on the loss function. When the preset training condition is met, the training of the initial differential pressure model is completed, and a trained differential pressure model is obtained. Among them, the preset training condition can be that the loss function converges, the number of iterations reaches a threshold, etc.

[0193] In some embodiments, the learning rates of different training sample sets of the differential pressure model are different, and the learning rate is confirmed based on the confidence of the training sample set.

[0194] In some embodiments, the processor may divide multiple training samples into multiple groups based on the adjustment time point. For example, multiple training samples are obtained based on historical sensing data including ( , ,...., ), where is the historical adjustment time point of the pressure regulating parameter, is the historical adjustment time point of the cooling parameter. Then, the training samples can be divided into 3 groups according to the historical time points, namely the 3 groups of training samples whose sample time points belong to ( , ,...., ), ( , ,...., ), ( , ,...., ).

[0195] In some embodiments, the processor may determine multiple fluctuation results corresponding to multiple groups of training samples; based on the multiple fluctuation results, determine the multiple confidences of the multiple groups of training samples. For this part, reference can be made to the content of determining the confidence of the sensing speed data in step S330. The process is similar and will not be elaborated here.

[0196] In some embodiments, the processor and / or the technician may perform interval division on the multiple confidences of multiple groups of training samples. For example, equally divide them into confidence intervals (0, 10%), (10%, 20%), etc.

[0197] In some embodiments, the processor may divide multiple groups of training samples with confidences in the same confidence interval into one training sample set, and multiple confidence intervals correspond to multiple training sample sets.

[0198] In some embodiments, the learning rates of different training sample sets of the differential pressure model are different. One training sample set may correspond to one learning rate, and the learning rate of one training sample set can be confirmed based on the confidence of the training sample set. For example, the learning rate of one training sample set may be positively correlated with the mean of the confidences of all groups of training samples in the training sample set. Exemplarily, the learning rate can be calculated based on the following formula (2): (2) Where is the learning rate, is the positive correlation coefficient, is the mean of the confidences.

[0199] In some embodiments of this specification, different training sample sets are selected for training the differential pressure model, and the learning rates of different training sets are different. The learning rate is one of the key factors affecting the training speed and stability of the model. The learning rate is adjusted according to the confidence level, enabling the model to learn quickly from the training sample set with high confidence and improving the reliability of the model.

[0200] In some embodiments, as Figure 5 shown, the cooling parameter 532 for the future time period output by the differential pressure model 520 further includes the refrigerant valve opening 532-3 of the expansion refrigeration device.

[0201] For more content regarding the refrigerant valve opening, reference can be made to the relevant description above.

[0202] In some embodiments, the training labels corresponding to the training samples of the differential pressure model may include the actual pressure regulating parameters, actual cooling parameters, and actual refrigerant valve openings during the sample time period. For the description of obtaining the training labels, reference can be made to the relevant description above. The process is similar and will not be elaborated here.

[0203] In some embodiments of this specification, the refrigerant valve opening directly controls the flow rate and pressure of the refrigerant in the refrigeration system, thereby affecting the refrigeration effect and energy consumption. By accurately calculating and outputting the optimal refrigerant valve opening through the differential pressure model, it can ensure that the refrigeration system operates efficiently under different conditions, meeting the cooling requirements while reducing energy consumption.

[0204] In some embodiments, as Figure 5 shown, the input of the differential pressure model 520 may further include the target gas temperature 515, and the cooling parameter for the future time period output by the differential pressure model may further include the refrigerant circulation speed 532-2 corresponding to the target gas temperature 515.

[0205] The target gas temperature refers to the expected value of the gas temperature after the gas passes through the pressure regulating device. For more content regarding the refrigerant circulation speed, reference can be made to the relevant description above.

[0206] In some embodiments, the training samples of the differential pressure model further include the sample target gas temperature, and the training labels further include the actual refrigerant circulation speed at the sample target gas temperature corresponding to the training samples. In some embodiments, the processor may train the differential pressure model based on multiple training samples including the sample target gas temperature. The training process can be referred to above and will not be elaborated here.

[0207] In some embodiments of the present specification, the target gas temperature is used as an input to the differential pressure model, so that the corresponding refrigerant circulation speed can be accurately determined through the differential pressure model, avoiding problems such as increased system pressure loss due to too high a circulation speed and poor refrigeration effect due to too low a circulation speed, thereby optimizing the refrigeration performance of the system as a whole and enhancing the stability of the system.

[0208] In some embodiments of the present specification, by comprehensively considering various data (such as the first pressure of multiple input pipelines, gas temperature, etc.), the pressure regulation parameters and cooling parameters for multiple future time periods are accurately predicted at one time using a machine learning model, so as to make preparations for parameter adjustment in advance, which is beneficial to reducing energy waste and improving gas transportation efficiency and economic benefits.

[0209] In some embodiments, the cooling parameter may further include the refrigerant circulation speed. The processor can obtain the sensing data for the second time interval; in response to the sensing data meeting the adjustment condition, the refrigerant circulation speed at the second moment is adjusted, and the adjustment condition is at least one of the equipment temperature continuously rising, the fluctuation value of the second pressure exceeding the preset fluctuation threshold, and the gas temperature being higher than the preset gas temperature.

[0210] In some embodiments, the second time intervals of different gas pressure regulation stations are different. In some embodiments, the second time interval may be related to the first pressure, target output pressure, and target gas temperature corresponding to the gas pressure regulation station.

[0211] For more information about the above-mentioned multiple data and parameters (such as sensing data, first pressure, second pressure, refrigerant circulation speed, second time interval, target output pressure, etc.), reference can be made to the relevant descriptions above.

[0212] In some embodiments, the target output pressures of different gas pressure regulation stations are different. For example, the gas pressure regulation station located at the head of the pressure regulation process has a relatively higher target output pressure; the gas pressure regulation station located at the tail of the pressure regulation process has a relatively lower target output pressure.

[0213] In some embodiments, since the target output pressures of the gas pressure regulation stations are different, the corresponding second time intervals are also different. The processor can determine the second time interval by querying the second preset table based on the first pressure, target output pressure, and target gas temperature of the current gas pressure regulation station. Among them, the second preset table may include the first pressure, target output pressure, target gas temperature of multiple gas pressure regulation stations and the corresponding multiple second time intervals. The smaller the difference between the first pressure and the target output pressure and the higher the target gas temperature, the larger the second time interval. In some embodiments, the second preset table can be constructed by technicians based on historical data or historical experience.

[0214] In some embodiments of this specification, based on the first pressure, target output pressure, and target gas temperature of the current gas pressure regulating station, an appropriate second time interval can be determined to timely adjust the operating parameters of the equipment at an appropriate time, reducing the risk of failures in the gas pipeline network.

[0215] In some embodiments, the sensing data of the second time interval is monitored and obtained by a sensing device and uploaded to the gas company management platform through the gas company sensing network platform. The processor can directly retrieve the sensing data of the second time interval.

[0216] The adjustment condition refers to the condition for adjusting the refrigerant circulation speed. In some embodiments, the adjustment condition may include at least one of the equipment temperature continuously rising, the fluctuation value of the second pressure exceeding a preset fluctuation threshold, and the gas temperature being higher than a preset gas temperature.

[0217] In some embodiments, the second time interval may include multiple time periods, and one time period corresponds to a change speed of the equipment temperature. The equipment temperature continuously rising may mean that within the second time interval, the proportion of the number of time periods in which the change speed of the equipment temperature exceeds the average value of the change speed of the equipment temperature is greater than a proportion threshold. The average value of the change speed of the equipment temperature is the average of the change speeds of the equipment temperature in all time periods within the second time interval. The proportion threshold can be default set by the processor or set by a technician based on historical experience. For example, 80%.

[0218] In some embodiments, the second time interval may include multiple time points, and one time point corresponds to a second pressure. The fluctuation value of the second pressure can be represented by the standard deviation or variance of multiple second pressures at multiple time points within the second time interval.

[0219] In some embodiments, the processor can determine the preset fluctuation threshold based on the historical second pressure in the historical sensing data. For example, the processor can select a historical time period with the same duration as the second time interval, determine the historical sensing data of this historical time period, calculate the standard deviation of multiple historical second pressures in the historical sensing data, and determine this standard deviation as the preset fluctuation threshold. The fluctuation value of the second pressure exceeding the preset fluctuation threshold indicates that the second pressure fluctuates greatly and the refrigerant circulation speed needs to be adjusted in a timely manner.

[0220] The preset gas temperature refers to the gas temperature required for the output pipeline to deliver gas to downstream users. The gas temperature being higher than the preset gas temperature indicates that the gas temperature passing through the gas pressure regulating station is too high and the refrigerant circulation speed needs to be adjusted in a timely manner.

[0221] For more information about the change speed of the equipment temperature, the second pressure, the gas temperature, the output pipeline, and downstream users, reference can be made to the relevant descriptions above.

[0222] In some embodiments, when one or more of the following occur: the device temperature continues to rise, the fluctuation value of the second pressure exceeds a preset fluctuation threshold, and the gas temperature is higher than a preset gas temperature, the processor may determine that the sensing data for the second time interval meets the adjustment condition.

[0223] The second moment refers to the time point after the current moment has elapsed by the second time interval.

[0224] In some embodiments, in response to the sensing data for the second time interval meeting the adjustment condition, the processor may determine the refrigerant circulation speed at the second moment by retrieving a vector database.

[0225] Among them, the vector database includes a plurality of reference vectors and corresponding vector tags. The reference vector is composed of reference sensing data, reference pressure regulation parameters, and reference cooling working power for a reference time period. The vector tag is the reference refrigerant circulation speed corresponding to the reference vector. For more content regarding the cooling working power, reference may be made to the relevant description above.

[0226] In some embodiments, the vector database may be constructed based on historical data. For example, the processor may calculate the effectiveness scores of the historical sensing data for multiple historical time periods, select the historical sensing data with an effectiveness score greater than the score threshold as the reference sensing data, and determine the corresponding historical pressure regulation parameters and historical cooling working power as the reference pressure regulation parameters and reference cooling working power, thereby determining the reference vector. The vector tag corresponding to the reference vector is the historical refrigerant circulation speed. The historical time period is the same as the duration of the second time interval.

[0227] The effectiveness score refers to the score of the operation effectiveness of the gas pressure regulating station. In some embodiments, the processor may determine the effectiveness score based on the device temperature rise score, fluctuation score, gas temperature score, and pressure regulation score.

[0228] Among them, the device temperature rise score refers to the score of the device temperature change rate for the historical time period. In some embodiments, the device temperature rise score is negatively correlated with the device temperature change rate. The device temperature change rate includes positive and negative values. The greater the device temperature change rate, the lower the corresponding device temperature rise score. For more content regarding the device temperature change rate, reference may be made to the relevant description above.

[0229] The fluctuation score refers to the score of the fluctuation value of the second pressure for the historical time period. In some embodiments, the fluctuation score is negatively correlated with the fluctuation value of the second pressure. The greater the fluctuation value of the second pressure, the lower the corresponding fluctuation score. For more content regarding the fluctuation value of the second pressure, reference may be made to the relevant description above.

[0230] The gas temperature score refers to the score of the gas temperature change rate in a historical time period. In some embodiments, the gas temperature score is negatively correlated with the gas temperature change rate, and the gas temperature change rate includes positive and negative values. The greater the gas temperature change rate, the lower the corresponding gas temperature score. For more information about the gas temperature change rate, please refer to the relevant description above.

[0231] The pressure regulation score refers to the score of the pressure change rate in a historical time period. In some embodiments, the pressure regulation score is positively correlated with the pressure change rate. The greater the absolute value of the pressure change rate, the higher the corresponding pressure regulation score.

[0232] In some embodiments, the processor can perform a weighted sum of the device temperature rise score, the fluctuation score, the gas temperature score, and the pressure regulation score, and use the weighted sum result as the effect score. In the weighted sum, the weight of the pressure regulation score is the highest.

[0233] In some embodiments, the processor can construct a vector to be matched based on the sensing data, the pressure regulation parameters, and the cooling working power in a second time interval, calculate the similarities between the vector to be matched and multiple reference vectors, and determine the vector label corresponding to the reference vector with the highest similarity as the refrigerant circulation speed at the second moment.

[0234] In some embodiments, the processor can adjust the adjustment conditions based on the maintenance frequency of the gas pressure regulation station.

[0235] The maintenance frequency refers to the frequency of maintaining the equipment in the gas pressure regulation station. For example, the maintenance frequency of pipelines, cold energy recovery equipment, and pressure regulation equipment in the gas pressure regulation station.

[0236] In some embodiments, the maintenance frequency can be obtained by the gas equipment object platform, uploaded to the gas company management platform through the gas company sensing network platform, and directly retrieved by the processor.

[0237] In some embodiments, for a gas pressure regulation station with a high maintenance frequency, the processor can determine the reduction percentage of the adjustment conditions by querying a third preset table. The third preset table can include the maintenance frequency and the corresponding reduction percentage of the adjustment conditions.

[0238] The reduction percentage of the adjustment conditions can include the reduction percentage of the proportional threshold, the reduction percentage of the preset fluctuation threshold, and the reduction percentage of the preset gas temperature. Exemplarily, if the proportional threshold is 80% and the reduction percentage of the proportional threshold is 20%, the adjusted proportional threshold is 64%. The third preset table can be constructed by the processor and / or technicians based on historical data or historical experience.

[0239] For gas pressure regulating stations with high maintenance frequency, the processor can lower the proportional threshold, lower the preset fluctuation threshold, lower the preset gas temperature, lower the adjustment conditions, and improve the adjustment accuracy to ensure that the refrigerant circulation speed is adjusted in time when abnormal data occurs.

[0240] In the embodiments of this specification, the adjustment conditions are adjusted based on the maintenance frequency of the gas pressure regulating station. The gas company management platform can determine more stringent adjustment conditions when there are many problems with the equipment. By adjusting the refrigerant circulation speed, the equipment temperature change, the second pressure and the gas temperature are maintained in a relatively stable state, thereby more accurately controlling the output temperature and output pressure of the gas and reducing abnormal gas transmission conditions.

[0241] In the embodiments of the present specification, after adjusting the pressure regulating parameters and the cooling parameters, the operating effects of the pressure regulating equipment and the cold energy recovery equipment under the adjusted pressure regulating parameters and the cooling parameters can be evaluated by monitoring the sensor data at the second time interval again; and based on the equipment temperature change, the fluctuation value of the second pressure and the gas temperature change in the sensor data, the refrigerant circulation speed is adjusted, which is conducive to timely response to abnormal situations and avoid abnormalities in the gas pressure regulation work.

[0242] In some embodiments, a computer-readable storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes a method for joint control of equipment for safe gas pressure regulation.

[0243] The embodiments of the present invention are only for illustration and description, and do not limit the applicable scope of the present invention. For those skilled in the art, various modifications and changes that can be made under the guidance of the present invention are still within the scope of the present invention.

[0244] Furthermore, certain features, structures or characteristics in one or more embodiments of the present invention may be appropriately combined.

Claims

1. An equipment joint control Internet of Things system for gas safety pressure regulation, characterized in that: The Internet of Things system includes a gas company management platform and a gas equipment object platform; The gas equipment object platform includes a pressure regulating device and a cold energy recovery device, the pressure regulating device and the cold energy recovery device are equipped with a sensor device, and the pressure regulating device is arranged in a gas pressure regulating station; The gas company management platform is configured as follows: Acquire historical sensing data of the sensing device, the historical sensing data including sensing data at multiple historical moments, the sensing data including a first pressure of the gas before passing through the pressure regulating device, a second pressure of the gas after passing through the pressure regulating device, a gas temperature, and a device temperature of the pressure regulating device; Based on the historical sensor data, determining multiple sets of sensor statistical data, the sensor statistical data including pressure difference statistics and temperature difference statistics; as well as Based on the current sensor data and the sensor statistical data, an adjustment instruction is determined, and the adjustment instruction is configured to adjust a cooling parameter of the cold energy recovery device and a voltage regulating parameter of the voltage regulating device.

2. The equipment joint control Internet of Things system for gas safety pressure regulation according to claim 1 is characterized in that: The gas equipment object platform also includes a plurality of input pipelines of the gas pressure regulating station, the pressure regulating equipment includes a distribution equipment, a boosting equipment and / or a pressure reducing equipment, and the pressure regulating parameters include a distribution parameter of the distribution equipment, a boosting parameter of the boosting equipment and / or a pressure reducing parameter of the pressure reducing equipment; The gas company management platform is further configured as follows: Determine an initial pressure regulating parameter based on the first pressures and the target output pressures corresponding to the input pipelines, and control the pressure regulating device to operate based on the initial pressure regulating parameter; Acquiring the sensing data after a first time interval; Based on the sensor data, the voltage regulation parameter at a first moment is determined.

3. The equipment joint control Internet of Things system for gas safety pressure regulation according to claim 2 is characterized in that: The gas company management platform is further configured as follows: Determine the target output pressure based on the gas demand of the output pipeline; Based on the multiple first pressures corresponding to the multiple input pipelines, the target output pressure, and the gas temperature, the pressure regulation parameters and the cooling parameters for a future period are determined through a pressure difference model, and the pressure difference model is a machine learning model.

4. The equipment joint control Internet of Things system for gas safety pressure regulation according to claim 1 is characterized in that: The cooling parameters also include a refrigerant circulation speed, and the gas company management platform is further configured as follows: Acquiring the sensing data at a second time interval; In response to the sensor data satisfying an adjustment condition, the refrigerant circulation speed at the second moment is adjusted, and the adjustment condition is at least one of the following: the equipment temperature continues to rise, the fluctuation value of the second pressure exceeds a preset fluctuation threshold, and the gas temperature is higher than a preset gas temperature.

5. The equipment joint control Internet of Things system for gas safety pressure regulation according to claim 1 is characterized in that: The Internet of Things system also includes a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision object platform, and a gas company sensor network platform; the government safety supervision object platform includes the gas company management platform.

6. A device joint control method for gas safety pressure regulation, characterized in that: The method is executed by a gas company management platform of an equipment joint control Internet of Things system for gas safety pressure regulation, and the method comprises: Acquire historical sensing data of the sensing device, wherein the historical sensing data includes sensing data at multiple historical moments, and the sensing data includes a first pressure of the gas before passing through the pressure regulating device, a second pressure of the gas after passing through the pressure regulating device, a gas temperature, and a device temperature of the pressure regulating device; Determining a plurality of sets of sensory statistical data based on the historical sensory data, the sensory statistical data including pressure difference statistics and temperature difference statistics; and Based on the current sensor data and the sensor statistical data, an adjustment instruction is determined, and the adjustment instruction is configured to adjust a cooling parameter of the cold energy recovery device and a voltage regulating parameter of the voltage regulating device.

7. The method according to claim 6, characterized in that The Internet of Things system also includes a plurality of input pipelines of a gas pressure regulating station, the pressure regulating equipment includes a distribution equipment, a boosting equipment and a pressure reducing equipment, and the pressure regulating parameters include a distribution parameter of the distribution equipment, a boosting parameter of the boosting equipment and a pressure reducing parameter of the pressure reducing equipment; The method further comprises: Determine an initial pressure regulating parameter based on the first pressures and the target output pressures corresponding to the input pipelines, and control the pressure regulating device to operate based on the initial pressure regulating parameter; Acquiring the sensing data after a first time interval; Based on the sensor data, the voltage regulation parameter at a first moment is determined.

8. The method according to claim 7, characterized in that The method further comprises: Determine the target output pressure based on the gas demand of the output pipeline; Based on the multiple first pressures corresponding to the multiple input pipelines, the target output pressure, and the gas temperature, the pressure regulation parameters and the cooling parameters for a future period are determined through a pressure difference model, and the pressure difference model is a machine learning model.

9. The method according to claim 6, characterized in that The cooling parameter also includes a refrigerant circulation speed, and the method further includes: Acquiring the sensing data at a second time interval; In response to the sensor data satisfying an adjustment condition, the refrigerant circulation speed at the second moment is adjusted, and the adjustment condition is at least one of the following: the equipment temperature continues to rise, the fluctuation value of the second pressure exceeds a preset fluctuation threshold, and the gas temperature is higher than a preset gas temperature.

10. A computer-readable storage medium, characterized in that: The medium stores computer instructions, and when the computer reads the computer instructions in the medium, the computer executes the equipment joint control method for safe gas pressure regulation as described in any one of claims 6-9.

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